caffe学习笔记9 -- Brewing Logistic Regression then Going Deeper

这是caffe官方文档Notebook Examples中的第三个例子,将caffe用于逻辑回归分类。参考网址:http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/02-brewing-logreg.ipynb

虽然caffe本身为深度网络设计,但是,它也可以用在表示浅层模型中,我们用人工合成的数据做简单的逻辑回归,并且保存为HDF5作为caffe的输入向量。当模型建成后,添加层数提高准确率,caffe需要做的是:定义模型,实验然后应用。


1. 导入包

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import os
os.chdir('..')

import sys
sys.path.insert(0, './python')
import caffe


import os
import h5py
import shutil
import tempfile

import sklearn
import sklearn.datasets
import sklearn.linear_model

import pandas as pd

2. 合成10000个4维向量用于二分类,包含两个信息特征和两个噪声特征

X, y = sklearn.datasets.make_classification(
    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, 
    n_clusters_per_class=2, hypercube=False, random_state=0
)

# Split into train and test
X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)

# Visualize sample of the data
ind = np.random.permutation(X.shape[0])[:1000]
df = pd.DataFrame(X[ind])
_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])

图像如下:


3. 使用sklearn包中的逻辑回归方法训练数据,使用随机梯度下降(SGD)优化。检查分类器的准确性

%%timeit
# Train and test the scikit-learn SGD logistic regression.
clf = sklearn.linear_model.SGDClassifier(
    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')

clf.fit(X, y)
yt_pred = clf.predict(Xt)
print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))
结果:

Accuracy: 0.512
Accuracy: 0.512
Accuracy: 0.512
Accuracy: 0.512
1 loops, best of 3: 379 ms per loop

4. 将数据保存到HDF5中方便caffe使用

# Write out the data to HDF5 files in a temp directory.
# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb
dirname = os.path.abspath('./examples/hdf5_classification/data')
if not os.path.exists(dirname):
    os.makedirs(dirname)

train_filename = os.path.join(dirname, 'train.h5')
test_filename = os.path.join(dirname, 'test.h5')

# HDF5DataLayer source should be a file containing a list of HDF5 filenames.
# To show this off, we'll list the same data file twice.
with h5py.File(train_filename, 'w') as f:
    f['data'] = X
    f['label'] = y.astype(np.float32)
with open(os.path.join(dirname, 'train.txt'), 'w') as f:
    f.write(train_filename + '\n')
    f.write(train_filename + '\n')
    
# HDF5 is pretty efficient, but can be further compressed.
comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}
with h5py.File(test_filename, 'w') as f:
    f.create_dataset('data', data=Xt, **comp_kwargs)
    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)
with open(os.path.join(dirname, 'test.txt'), 'w') as f:
    f.write(test_filename + '\n')

5. 使用Python网络规范在caffe中定义逻辑回归

from caffe import layers as L
from caffe import params as P

def logreg(hdf5, batch_size):
    # logistic regression: data, matrix multiplication, and 2-class softmax loss
    n = caffe.NetSpec()
    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)
    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))
    n.accuracy = L.Accuracy(n.ip1, n.label)
    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)
    return n.to_proto()
    
with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:
    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))
    
with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:
    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))

6. 开始学习评估caffe中的逻辑回归方法:

%%timeit
caffe.set_mode_cpu()
solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')
solver.solve()

accuracy = 0
batch_size = solver.test_nets[0].blobs['data'].num
test_iters = int(len(Xt) / batch_size)
for i in range(test_iters):
    solver.test_nets[0].forward()
    accuracy += solver.test_nets[0].blobs['accuracy'].data
accuracy /= test_iters

print("Accuracy: {:.3f}".format(accuracy))

Accuracy: 0.496
Accuracy: 0.496
Accuracy: 0.496
Accuracy: 0.496
1 loops, best of 3: 121 ms per loop

7. 使用命令行查看模型和求解的全过程

!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt
输出省略。。。。。。


8. 上面的模型logreg_auto_train.prototxt,是一个简单的逻辑回归模型,可以通过在输入与输出的权重之间引入非线性改进算法,将这个网络变为两层,网络结构定义在nonlinear_auto_train.prototxt中。

from caffe import layers as L
from caffe import params as P

def nonlinear_net(hdf5, batch_size):
    # one small nonlinearity, one leap for model kind
    n = caffe.NetSpec()
    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)
    # define a hidden layer of dimension 40
    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))
    # transform the output through the ReLU (rectified linear) non-linearity
    n.relu1 = L.ReLU(n.ip1, in_place=True)
    # score the (now non-linear) features
    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))
    # same accuracy and loss as before
    n.accuracy = L.Accuracy(n.ip2, n.label)
    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
    return n.to_proto()
    
with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:
    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))
    
with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:
    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))

9. 评估模型

%%timeit
caffe.set_mode_cpu()
solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')
solver.solve()

accuracy = 0
batch_size = solver.test_nets[0].blobs['data'].num
test_iters = int(len(Xt) / batch_size)
for i in range(test_iters):
    solver.test_nets[0].forward()
    accuracy += solver.test_nets[0].blobs['accuracy'].data
accuracy /= test_iters

print("Accuracy: {:.3f}".format(accuracy))

Accuracy: 0.836
Accuracy: 0.839
Accuracy: 0.838
Accuracy: 0.840
1 loops, best of 3: 194 ms per loop

10. 与之前相同,查看详细的结构

!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt

11. 清除数据

shutil.rmtree(dirname)


参考网站:

http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/02-brewing-logreg.ipynb

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